Data Anomaly Detection in the Internet of Things: A Review of Current Trends and Research Challenges

Abstract

The Internet of Things (IoT) has revolutionized how we interact with the physical world, bringing a new era of connectivity. Billions of interconnected devices seamlessly communicate, generating an unprecedented volume of data. However, the dramatic growth of IoT applications also raises an important issue: the reliability and security of IoT data. Data anomaly detection plays a pivotal role in addressing this critical issue, allowing for identifying abnormal patterns, deviations, and malicious activities within IoT data. This paper discusses the current trends, methodologies, and challenges in data anomaly detection within the IoT domain. In this paper, we discuss the strengths and limitations of various anomaly detection techniques, such as statistical methods, machine learning algorithms, and deep learning methods. IoT data anomaly detection carries unique characteristics and challenges that must be carefully considered. We explore these intricacies, such as data heterogeneity, scalability, real-time processing, and privacy concerns. By delving into these challenges, we provide a holistic understanding of the complexity associated with IoT data anomaly detection, paving the way for more targeted and effective solutions.